ABSTRACT
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide, leading to cognitive decline and memory loss. Despite extensive research, finding effective treatments for AD remains a significant challenge. However, recent developments in computer-aided drug design (CADD) have opened opportunities that promise to accelerate the discovery of new AD therapeutics. With the aid of high-performance computing and advanced algorithms, researchers have been able to identify key protein targets implicated in AD pathology, such as amyloid-beta (Aβ) plaques and tau proteins. This knowledge has enabled the design of molecules that can modulate these targets, thereby potentially slowing down disease progression. CADD platforms employ virtual libraries comprising millions of compounds that can be rapidly screened against target proteins. The integration of computational models, virtual screening, machine learning, and experimental approaches has allowed for the identification and optimization of potential therapeutic agents with improved efficacy and safety profiles.
